Quantitative fluoroscopic dose saving in cardiovascular imaging with a novel motion discriminating temporal filter

Manjeshwar, Ravindra; Dhawale, P.J.

doi:10.1109/cic.2005.1588078

Cited by 4 publications

(7 citation statements)

References 2 publications

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“…Several sequences are computed for different input guide wire CNR ranging from 0.3 to 5. We compare our algorithm to Temporal motion adapted IIR (TIIR) [10], dual tree complex Wavelet Shrinkage denoising (WS) and the combination of both (ST) [7]. On the one hand we present the results for a given input sequence of CN R = 2.…”

Section: Quantitative Evaluation and Application To Clinical Datamentioning

confidence: 98%

“…The setting of the shrinkage thresholds are band dependant and can be straightforwardly derived from the noise power spectrum based on the above mentionned constraint. As for temporal filtering we rely on motion adaptive IIR, similar to the one described in [10]. The current denoised background image, denoted BG t is obtained by applying the motion adaptive temporal IIR to the wavelet denoised image sequence.…”

Section: Background Denoisingmentioning

confidence: 99%

“…Moreover the noise does not follow the additive white Gaussian noise model but is signal dependant and spatially correlated (by the imaging system MTF). Many adaptations of techniques for multimedia sequences to the specificities of FISIC have been proposed, including temporal filtering [10], spatial filtering [9], multi-resolution [6] and motion compensated denoising [2]. However, most efficient techniques combine spatial filtering on moving areas with temporal filtering in static areas.…”

Section: Introductionmentioning

confidence: 99%

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A device enhancing and denoising algorithm for X-ray cardiac fluoroscopy

Bismuth¹,

Vaillant²

2008

2008 19th International Conference on Pattern Recognition

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In X-ray interventional imaging, improved denoising enables lower X-ray dose and better visualization, resulting in increased confidence in the therapeutic act. This article focuses on the denoising of fluoroscopic image sequences in interventional cardiology. The challenge with these images is the need to preserve fine details in terms of size and contrast, representing the tools manipulated by the operator. These tools superimpose over an anatomical background. This context drives us to propose an algorithm based on spatial-temporal filtering conditioned by a feature of interest map. Based on the confidence in the feature detection our algorithm will either filter, preserve or enhance the image content. Our method is fast and compares very favorably with state of the art methods both quantitatively on synthetic data and perceptually on clinical data.

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Section: Quantitative Evaluation and Application To Clinical Datamentioning

confidence: 98%

Section: Background Denoisingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A device enhancing and denoising algorithm for X-ray cardiac fluoroscopy

Bismuth¹,

Vaillant²

2008

2008 19th International Conference on Pattern Recognition

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“…Image intensifiers coupled to analogous cameras may have as much as 20% lag. 9 The lag degrades the temporal resolution of the dynamic image. However, lag also increases quality in images where there is no motion because images are integrated, which again reduces the quantum noise (averaging uncorrelated noise).…”

Section: Introductionmentioning

confidence: 99%

“…A β-factor describes weighting in a simple temporal recursive filtering method, extensively used in fluoroscopy. 9 Recursive filtering is often user adjustable and has effects on both the correlated and the noncorrelated part of the NPS.…”

Section: Introductionmentioning

confidence: 99%

Novel method to determine recursive filtration and noise reduction in fluoroscopic imaging – a comparison of four different vendors

Konst

Nøtthellen

Naess

et al. 2020

J Applied Clin Med Phys

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PurposeThis study attempted to develop a method to measure the applied recursive filtration and to determine the noise reduction of four different fluoroscopic systems. The study also attempted to elucidate the importance of considering the recursive filter for quality control tests concerning signal‐to‐noise ratio (SNR) or image quality. The vendor’s settings for recursive filtration factor (β) are, unfortunately, often not available. Hence, a method to determine the recursive filtration and associated noise reduction would be useful.MethodThe recursive filter was determined by using a single fluoroscopic series and the method presented in this study. The theoretical noise reduction based on the choice of β was presented. In addition, the corresponding noise reduction, evaluated as the ratio of the standard deviation of the pixel value between a series with β equal to zero (recursive filtration off) and β > 0, was determined for different pulse rates given by pulses per second (pps), doses (mAs) and recursive filter. The images were acquired using clinically relevant radiation quality and quantity.ResultsThe presented method to measure the recursive filter exhibited high accuracy (1.08%) and precision (1.48%). The recursive filtration and noise reduction were measured for several settings for each vendor. The recursive filtration settings and associated recursive filtration factors for four different vendors were presented.ConclusionsThis study presented an accurate method to determine applied recursive filtration, which was easy to determine. Hence, for all quality control purposes, including noise evaluation, it was possible to consider the essential noise reduction given by the settings for recursive filtration. It was also possible to compare the recursive filtration settings and associated recursive filtration within and between vendors.

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